Wideband compressive beamforming tomography for drive-by large-scale acoustic source mapping.

Noise-mapping is an effective sound visualization tool for the identification of urban noise hotspots, which is crucial to taking targeted measures to tackle environmental noise pollution. This paper develops a high-resolution wideband acoustic source mapping methodology using a portable microphone array, where the joint localization and power spectrum estimation of individual sources sparsely distributed over a large region are achieved by tomographic imaging with the multi-frequency delay-and-sum beamforming power outputs from multiple array positions. Exploiting the fact that a wideband source has a common spatial signal-support across the frequency spectrum, two-dimensional tomographic maps are produced by applying compressive sensing techniques including group least absolute shrinkage selection operator formulation and sparse Bayesian learning to promote group sparsity over multiple frequency bands. The high-resolution mapping is demonstrated with experimental data recorded with a microphone array mounted atop an electric vehicle driven along a road while playing audio clips from a loudspeaker positioned within the adjacent open field.

Large-region acoustic source mapping using a movable array and sparse covariance fitting.

Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)].

Drive-by large-region acoustic noise-source mapping via sparse beamforming tomography.

Environmental noise is a risk factor for human physical and mental health, demanding an efficient large-scale noise-monitoring scheme. The current technology, however, involves extensive sound pressure level (SPL) measurements at a dense grid of locations, making it impractical on a city-wide scale. This paper presents an alternative approach using a microphone array mounted on a moving vehicle to generate two-dimensional acoustic tomographic maps that yield the locations and SPLs of the noise-sources sparsely distributed in the neighborhood traveled by the vehicle. The far-field frequency-domain delay-and-sum beamforming output power values computed at multiple locations as the vehicle drives by are used as tomographic measurements. The proposed method is tested with acoustic data collected by driving an electric vehicle with a rooftop-mounted microphone array along a straight road next to a large open field, on which various pre-recorded noise-sources were produced by a loudspeaker at different locations. The accuracy of the tomographic imaging results demonstrates the promise of this approach for rapid, low-cost environmental noise-monitoring.